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Cardoso, M. G. M. S., Martins, A. A. A. F. & Lagarto, J. (2019). Combining various dissimilarity measures for clustering electricity market prices. O XXIV Congresso da Sociedade Portuguesa de Estatística.

M. M. Cardoso et al.,  "Combining various dissimilarity measures for clustering electricity market prices", in O XXIV Congr.o da Sociedade Portuguesa de Estatística, 2019

@misc{cardoso2019_1732202057129,
	author = "Cardoso, M. G. M. S. and Martins, A. A. A. F. and Lagarto, J.",
	title = "Combining various dissimilarity measures for clustering electricity market prices",
	year = "2019"
}

TY  - CPAPER
TI  - Combining various dissimilarity measures for clustering electricity market prices
T2  - O XXIV Congresso da Sociedade Portuguesa de Estatística
AU  - Cardoso, M. G. M. S.
AU  - Martins, A. A. A. F.
AU  - Lagarto, J.
PY  - 2019
AB  - The development of an internal market of electricity has long been a goal
of the European Union, since it enables European citizens and businesses to choose
their supplier, creates new business opportunities and enhances cross-border trade
with the purpose of ensuring effciency gains and competitive prices and, contribute
to security of supply and sustainability.
In order to better understand the degree of integration of the electricity markets
of different European countries, we propose to cluster time series regarding each
country's day-ahead electricity market prices, investigating similar market prices
behaviours. The electricity markets in study are the MIBEL, the Italian, the Nordpool,
the French and the German markets. Hourly data are considered and were
obtained from the market operators' websites.
When clustering time series, the selection of the dissimilarity or a distance measure
is a key issue. In fact, different dissimilarity measures between time series have been
proposed in the literature (e.g [1]), each one revealing specific characteristics of the
data. For example, the Euclidean distance stresses the scale differences, measures
based on Pearson's correlation, periodogram or autocorrelation compare the time
series regarding their dynamic structure. In order to integrate, in the clustering
task, the diversity of available information, we propose to use a linear combination
of four dissimilarity measures: Euclidean, Pearson correlation, Periodogram and Autocorrelation
based measures. A sensitivity analysis, regarding the weights of the
various dissimilarity measures in the linear combination, is conducted to provide a
clear view of the weights impact in the clustering results.
The K-Medoids algorithm is used to form the clusters. The clustering solutions
are selected by simultaneously taking into-account visual representations of
partitions (e.g. [3]) and also resorting to cohesion-separation measures.
In the implementation we resort to software R and also use specific packages like
cluster , TSclust and fpc .
ER  -